Ham petrol fiyatlarının ANFIS ile tahmini

Yıl 2010, Sayı: 17, 3 - 14, 01.01.2010

Oğuz Kaynar Metin Zontul Ferhan Demirkoparan

Öz

Ekonomik kalkınmanın temel ağırlıklı girdisini oluşturan ve halen dünyadaki en önemli enerji kaynağı olma özelliğini koruyan petrol, doğrudan ya da dolaylı olarak tüm sektörleri etkilemektedir. Bu nedenle petrol piyasasında ve dolayısıyla fiyatında ortaya çıkan değişiklikler, oluşturdukları zincirleme reaksiyonlar aracılığı ile hem ülke, hem de dünya ekonomisi üzerinde çeşitli etkiler yaratmaktadır. Karmaşık dinamiklerinden dolayı, oldukça değişken ve etkileşimli bir yapıya sahip petrol piyasasında geleceğe yönelik etkili planlar yapmak için doğru ve güvenilir tahminlere gereksinim vardır. Bu çalışmada orta ve uzun vadeli petrol fiyatlarını tahmin etmek amacıyla bulanık çıkarım sistemleriyle yapay sinir ağlarının birleşiminden oluşan ANFIS (Adaptive Neuro Fuzzy Inference System) kullanılmıştır

Anahtar Kelimeler

ANFIS, Ham Petrol, Tahmin

Crude oil price forecasting by using ANFIS

Öz

Oil which is basic input of economic development and resumes to be the most important source of energy in the world, affects all sectors directly or indirectly. Consequently, the changes on petrol industry, and thus, on petrol prices create various effects on both country and world economy by means of chaining reactions turning up. For making affective plans for the future about petrol industry which has a considerably unsteady and interactive structure because of its complex dynamics, straight and confidential predictions are needed. So, ANFIS (Adaptive Neuro Fuzzy Inference System) which consists of integration of fuzzy inference systems and artificial neural networks is used to predict crude oil prices in middle and long term in this study

Anahtar Kelimeler

ANFIS, Crude oil, Forecasting

Kaynakça

• M. R. Amin-Naseri, E. A. Gharacheh,“A hybrid artificial intelligence approach to monthly forecasting oil price time series”, Proceedings of EANN, 2007.
• E. Uğurlu, A. Ünsal, “Ham Petrol İthalatı ve Ekonomik Büyüme: Türkiye”, 10. Ekonometri ve İstatistik Sempozyumu, Erzurum 27-29 Mayıs 2009.
• K.Aklin, S. Atman, Küresel Petrol Stratejilerinin Jeopolitik Açıdan Dünya ve Türkiye Üzerindeki Etkileri, İstanbul Ticaret Odası, İstanbul, 2008.
• M. A. Kaboudan, “Compumetric Forecasting Of Crude Oil Prices”, Proceedings of The 2001
• Congress on Evolutioanry Computation, 1, 2001.
• S. Abosedra, H. Baghetani, “On The Predictive Accuracy of Crude Oil Futures Prices”, Energy Policy, 32, 1389-1393, 2004.
• A. Bernabea, E. Martinaa, J. A. Ramirez, C. I. Valdez ,“A Multi-Model Approach for Describing Crude Oil Price Dynamics”, Physica A, 338, 567-584, 2004.
• S. Kulkarni, I. Haidar, “Forecasting Model For Crude Oil Price Using Artificial Neural Networks And Commodity Futures Prices”, International Journal of Computer Science and Information Security, 2(1), 2009.
• P. G. Harrald, M. Kamstra, “Evolving Artificial Neural Networks To Combine Financial Forecasts”, IEEE Transactions On Evolutionary Computation, 1(1), 40-52, 1997.
• H. Pan, I. Haidar, S. Kulkarni, “Daily Prediction Of Short Term Trends Of Crude Oil Prices Using Neural Networks Exploiting Multimarket Dynamics”, Front. Comput. Sci., 3(2), 177-191, A. Ghaffarı, S. Zare, “A Novel Algorithm For Prediction Of Crude Oil Price Variation Based On Soft Computing”, Energy Economics, 31, 531-536,2009.
• V. Fernandez, “Forecasting commodity prices by classification methods: The cases of crude oil andnatural gas spot prices”, Banco Central De Chile Conference, July 27, 2007.
• JS. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans Syst, Man, Cybernet”,23(3),665–685,1993.
• G. Cybenko, “Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems”, 2,303–314,1989.
• K. Hornik, M. Stinchcombe, H. White, “Multilayer feedforward networks are universal approximators.”, Neural Networks 2,359–366, 1989.
• K. Hornik, “Approximation capability of multilayer feedforward networks.” ,Neural Networks ,251–257, 1991.
• M. S. Chen, L. C. Ying, M. C. Pan “Forecasting tourist arrivals by using the adaptive network- based fuzzy inference system” , Expert Systems with Applications, 2009.
• M. Fırat, M. Güngör, “River Flow Estimation using Adaptive Neuro Fuzzy Inference System”, Mathematics and Computers in imulation,75(3-4), 87-96, 2007.
• P.C. Nayak, K.P. Sudheer, D.M. Ragan, K.S. Ramasastri, “A neuro fuzzy computing technique for modeling hydrological time series”, Journal of Hydrology”,29,52–66,2004.
• M.A. Yurdusev, M. Firat, “Neuro-Fuzzy Inference System and Artificial Neural Networks for Municipal Water Consumption Prediction”, Journal of Hydroinformatics, 365, 225-234, 2009.
• E.H. Mamdani, S. Assilian, ”An experiment in linguistic synthesis with a fuzzy logiccontroller”, Int. Journal of Man-Machine Studies, 7(1), 1-13, 1975.
• T. Takagi, Sugeno M. “Fuzzy identification of systems and its applications to modeling and control”,IEEE Trans. Syst., Man Cybern., 15,116–132, 1985.
• J.S.R. Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, United States of America, 1997.
• L.C. Ying, M.C. Pan “Using adaptive network based fuzzy inference system to forecast regional electricity loads”, Energy Conversion and Management, 49,205–211,2008.